Silicon oxide (SiO2) is a good dielectric material in metal-oxide-semiconductor (MOS) structures. The improved SiO2 quality requires adequate study of doping diffusion in this structure to maintain the absence of the different impurities in the interface Poylsilicon/SiO2. For this we studied a theoretical model of boron diffusion before and after thermal annealing in a highly-doped polysilicon films. The model takes into account the distribution of vacancy mechanism by associating parameters and effects related to high concentrations. Based on the literature the model is solved using the engineering software tool MATLAB, following a well-defined algorithm. The model is validated with the help of simulation results obtained from Silvaco.

An 8-bit 40-MS/s low power Multiplying Digital-toAnalog Converter (MDAC) for a pipelined-to-Analog to Digital converter (ADC) is presented.The conventional dedicated operational amplifier (Op-Amp) isperformed by using telescopic architecture that features low power and less-area. Further reduction of power and area is achieved by using multifunction 1.5bit/stage MDAC architecture. The design of the Op-Amp is performed by the elaboration of a program based on multiobjective genetic algorithms to allow automated optimization. The proposed program is used tofind the optimal transistors sizes (length and width) in order to obtain the best Op-Amp performances for the MDAC. In this study, six performances are considered, direct current gain, unity-gain bandwidth, phase margin, power consumption, area, slew rate, thermal noise, and signal to noise ratio. The Matlab optimization toolbox is used to implement the program. Simulations were performed by using Cadence Virtuoso Spectre circuit simulator in standard AMS 0.18μm CMOS technology. A goodagreement is observed between the results obtained bythe program optimization and simulation, after that the Op-Ampis introduced in the MDAC circuit to extract its performances.

Data aggregation has emerged as a basic concept in wireless sensor networks. By combining data and eliminating redundancy, this concept showed its effectiveness in terms of reducing resources consumption. The data aggregation problem is not new and has been widely studied in wireless sensor networks. However, most of the proposed solutions are based on a static data aggregation scheme and do not consider the network constraints evolution such as energy bandwidth, overhead and transmission delay. To dynamically optimize the tradeoff between the data aggregation process and the network constraints, a new paradigm has been introduced recently named the feedback control system. The main idea is to adapt the data aggregation degree to the environment changes and the sensor network applications. The feedback control for data aggregation remains relatively a new research area and only few works have introduced this concept. Therefore this work points out this research field by surveying the different existing protocols.

Safety instrumented systems must be designed, built and operated to meet tolerable risk level as required regulatory agencies. This requirement is closely related to their probabilistic performance measures which are either their average probability of dangerous failure on demand (PFDavg) or their average frequency of failure (PFH: Probability of Failure per Hour). The object of this work is the SIS performances evaluation taking into account the uncertainties related to the different parameters that come into play: failure rate (λ), common cause failure proportion (β), diagnostic coverage (DC), etc. This leads to an accurate and therefore safe assessment of the safety integrity level (SIL) inherent to safety functions performed by such systems. This aim is in keeping with the requirement of the IEC 61508 standard with respect to handling uncertainty. In this paper we first explain in detail the IEC 61508 approach for handling uncertainty. Afterwards, we propose an approach that combines (i) Monte Carlo analysis (MCA) and (ii) fuzzy sets. Indeed, the first method is appropriate when representative statistical data are available (using pdf of the relating parameters), while the latter applies in the case characterized by vague and subjective information (using membership function). The proposed approach is fully supported with a suitable computer code developed under the MATLAB environment.

We consider a system of transmission of the wave equation with Neumann feedback control that contains a delay term and acts on the exterior boundary. First, we prove under some assumptions that the closed-loop system generates a C 0 -semigroup of contractions on an appropriate Hilbert space. Then, under further assumptions, we show that the closed-loop system is exponentially stable. To establish this result, we introduce a suitable energy function and use multiplier method together with an estimate taken from Lasiecka & Triggiani (1992, Appl. Math. Optim., 25, 189–244.) (Lemma 7.2) and compactness-uniqueness argument.

In radiotherapy using 18-fluorodeoxyglucose positron emission tomography (18F-FDG-PET), the accurate delineation of the biological tumour volume (BTV) is a crucial step. In this study, the authors suggest a new approach to segment the BTV in 18F-FDG-PET images. The technique is based on the k-means clustering algorithm incorporating automatic optimal cluster number estimation, using intrinsic positron emission tomography image information. Clinical dataset of seven patients have a laryngeal tumour with the actual BTV defined by histology serves as a reference, were included in this study for the evaluation of results. Promising results obtained by the proposed approach with a mean error equal to (0.7%) compared with other existing methods in clinical routine, including fuzzy c-means with (35.58%), gradient-based method with (19.14%) and threshold-based methods.

Wavelets are known particularly to be an effective tool for extracting discriminative features in the scattered RF signals of both solid and gaseous emboli. However, the selection of an appropriate mother wavelet for the signal being analyzed is an important criterion. This offers the possibility to perform an optimization procedure to obtain the best wavelet. The purpose of the study is to propose a new approach to classify microembolic echoes using a discrete wavelet transform (DWT) based on genetic algorithm optimization and support vector machine (SVM) classifier. The experimental setup consists of a flow phantom (ATSLaB) containing a tube of 6 mm in diameter. In order to mimic the ultrasonic behavior of gaseous emboli, contrast agents consisting of microbubbles are used in our experimental setup. However, to mimic the behavior of the solid emboli we have used the Doppler fluid which contains particles with scatter characteristics comparable to red blood cells. The acquisitions are carried out at 2 MHz and 3.5 MHz transmit frequency. Ultrasound waves are transmitted at different intensities corresponding to mechanical indices (MI) of 0.21 and 0.42 for the transmit frequency of 2 MHz, and 0.31 and 0.62 for the transmit frequency of 3.5 MHz. Two concentrations of the contrast agent (100 μl and 200 μl) are diluted into a 100 ml volume of water. The polyphase representation of the discrete wavelet transform (DWT) is exploited in this study. Such representation allows generating a wavelet filter bank from a set of angular parameters, in order to minimize the fitness function based on genetic algorithm optimization and the SVM classifier. The best accuracy classifications of microemboli obtained in this study are equal to 99.90% for 2MHz and to 99.60% for 3.5MHz. These results illustrate that wavelet optimization approach works well for microemboli classification using RF signals.